Blang: Bayesian Declarative Modeling of General Data Structures and Inference via Algorithms Based on Distribution Continua

Blang: Bayesian Declarative Modeling of General Data Structures and Inference via Algorithms Based on Distribution Continua

Bayesian modeling language, Bayesian inference, non-standard data structures,
Blang.

Consider a Bayesian inference problem where a variable of interest does not take
values in a Euclidean space. These “non-standard” data structures are in reality fairly
common. They are frequently used in problems involving latent discrete factor models,
networks, and domain specific problems such as sequence alignments and reconstructions,
pedigrees, and phylogenies. In principle, Bayesian inference should be particularly wellsuited in such scenarios, as the Bayesian paradigm provides a principled way to obtain
confidence assessment for random variables of any type. However, much of the recent work
on making Bayesian analysis more accessible and computationally efficient has focused on
inference in Euclidean spaces.
In this paper, we introduce Blang, a domain specific language and library aimed at
bridging this gap. Blang allows users to perform Bayesian analysis on arbitrary data types
while using a declarative syntax similar to the popular family of probabilistic programming
languages, BUGS. Blang is augmented with intuitive language additions to create data
types of the user’s choosing. To perform inference at scale on such arbitrary state spaces,
Blang leverages recent advances in sequential Monte Carlo and non-reversible Markov
chain Monte Carlo methods

Alexandre Bouchard-Côté

https://www.jstatsoft.org/article/view/v103i11

University of British Columbia

July 2022

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